Face Image Transfer Learning for Body Fluid Volume Estimation
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Solution Overview
Problem
Existing methods for measuring body fluid volume, such as swelling, require professional medical staff and specialized equipment, limiting patients' ability to autonomously manage their fluid intake in daily life.
Innovation Solution
A body fluid volume estimation device utilizing pre-training and transfer learning on face images, employing WeightSupMoCo for contrastive learning with dialysis and weight labels, enabling patients to estimate fluid volume from their own images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If professional medical staff perform body fluid volume measurement using specialized equipment (pitting test, SWIR camera), then measurement precision is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent uses a standard camera to capture face images as a copy or substitute for specialized imaging equipment like SWIR cameras. The deep learning model processes these standard images to extract body fluid volume information, eliminating the need for expensive, complex specialized equipment while maintaining measurement capability.
Solution Approach 2:
The patent replaces manual physical examination methods (pitting test requiring medical staff) with an automated image processing system. The mechanical/manual measurement process is substituted by an electronic system using standard cameras and deep learning algorithms, reducing device complexity and enabling patient self-measurement.
2Measurement precision
If professional medical staff perform body fluid volume measurement, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent enables patients to perform body fluid volume measurements independently using standard cameras (e.g., smartphone cameras). Patients capture their own face images and the system automatically processes them through the deep learning model, eliminating the need for medical staff involvement and allowing self-service measurement.
Solution Approach 2:
The patent replaces the manual examination process requiring medical staff with an automated digital system. The system automatically captures, processes, and analyzes face images using deep learning, substituting the mechanical/manual operations of medical professionals with an autonomous electronic system that patients can operate themselves.
3Adaptability or versatility
If deep learning model is trained with limited patient-specific data, then adaptability is improved, but measurement precision may deteriorate
Solution Approach 1:
The patent performs pre-training of the deep learning model using a large dataset of face images from multiple patients before deployment. This preliminary training establishes a robust base model that can then be quickly adapted to individual patients with limited data through fine-tuning, ensuring both adaptability and precision are achieved.
Solution Approach 2:
The patent segments the training process into two distinct phases: pre-training on a large diverse dataset to learn general features, and subsequent fine-tuning on patient-specific data to adapt to individual characteristics. This segmentation allows the model to benefit from both large-scale learning and personalized adaptation, resolving the contradiction between adaptability and precision.
Data Source
AI summary
A body fluid volume estimation device includes a pre-training unit, a transfer learning unit, and an estimation unit. The pre-training unit performs pre-training by using, as supervised information, information indicating body fluid volumes of the multiple patients when face images of multiple patients are captured. The transfer learning unit further performs transfer learning on multiple face images of one specific patient after the pre-training, and constructs a trained model. The estimation unit estimates, by inputting a face image of the one specific patient to the trained model, a body fluid volume at a point in time at which the face image of the one specific patient is captured. By estimating a body fluid volume from a face image by machine learning, the body fluid volume can be used for assistance such as decision making of a user.